To realise edge intelligence, proposals for in-memory computing to completely overhaul the current von-Neumann architecture has gained immense interest. Brain-inspired neuromorphic computing using hardware-based neural networks (NN), comprising billions of neurons interconnected by an even larger number of synapses, aims to increase the efficiency of computationally intensive tasks like machine learning and pattern recognition. [1]}, [2]} Chiral spintronics hardware platforms are well suited to execute such tasks, as they are non-volatile, energy-efficient and exhibit a mixture of digital and analogue characteristics. Specifically, the solitonic, weakly interacting nature of skyrmions allow them to represent the indistinguishable, binary spike signals in a spiking NN, while their spatiotemporal current-driven motion can be used to intuitively track spike signal transmissions characteristic of information stored in the time domain (Overview). [3]}, [4]}
